{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data for plotting\n",
    "t = np.arange(0.0, 2.0, 0.01)\n",
    "s = 1 + np.sin(2 * np.pi * t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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N32dHEx3evS8hnoqrwa5EhnruC1FP+D96Wldiy87O3tZmSY2qeu0BpAN7OtjmRuDD85b1d/1MBXYBc905X1ZWlnbV6tWru7xvS402u64+cEptdodHjuepuDzNX+NS9d/YPBnXjqNn9M1NRzxyLE/E9euP9+usp1ap3UPXvWrP+D96WldiA7ZqG5+p/tCK6SbOK15S1eOun8XAe8BUC+Lqkg2HS7n91S2sPmDmiDa8Z8KgBG6aOtjqMM4a2TeOojN1bClwt1TZCASWJggRiQcuAv7eYlmMiMQ1/w5cDrTaEsofLdt1nLjIMGZnJlsdyjne33GM339yyOowDA9w+GHv+MtGpxEXGcYXRRVWh2J4kDebub4JbARGikiRiNwlIveJyH0tNrsO+Ieq1rRYlgasF5FdwGbgI1Vd4a04PW1An17cPG0wUeGeK+/3hO1Hz/DHNblmAL8AV1XfxKxff8qHfjYGUnREGBsevZh75mZYHYrhQWHeOrCqLnJjm9dwNodtuSwPGO+dqLzvoUtHWB1Cq66dOIDXNx5hxZ6T3DDZunbzRves2HOSExX1DOjTy+pQvsKKDqGGd/lDHYThAxMHJfDr6y/k4lGpVodidMPaQyUMToxmog8H5uuMkxX1pv4tiJgE4SH1TXZ+8t5u9p+otDqUVokIN04ZTFKsaa8eyH5/4wT+eu90y/vXtOXZ1Ye4/43tZp6IIGEShIesO1TCXzYd5VQATOZuhgAPXCEhQr94/ytearZgTD/qmuys/dKMIhwMTILwkGVfHCchOpxZw/2r9dL5HvjLdh54Y7vVYRhdsO94JU12h9VhtGtaRiLxvcJZsfek1aEYHmAShAfUN9n5ZN8pFozpS3iof7+kyTERrD5YTG2jzepQjE6obbTxjec/56mPD1gdSrvCQ0O49II0Ptl3yu+TmdEx//40CxCf55ZQ02jnigt9P+9DZ80f25cGm4PPDpoigECy9svT1Dc5uOQC/29kMH9MGpX1NnLySq0OxegmkyA84JP9p4iNDGN6RqLVoXRoanoifaLDWWmKAALKij0n6RMdztR0/7/G5o5IoVd4KCv2mGss0JkE0U0Oh/LJ/mIuGpFCZJh/dY5rTVhoCJeNTmPV/mIabaYIIBA02hys2l/MZaPTCPPzIkyAqPBQskelsHLvKdMgIsD5/9Xm5744VsHpqgYuHe3/t/7NFoztS1WDjQ2HS6wOxXDDhsMlVDXYWDDW99OKdtX8MX0pqW5gx1EzX3UgMwmimz7Zd4rQECF7ZOAkiJnDkomNDDPFTAFi5d6TxEaGMXOYf7eQa+niUalEhIaYYqYAZxJEN32y/xRT0vuQEB1hdShuiwoP5aIRKazaX2xmmvNzqsoqVxGmv43v1Z64qHCmD0tilelVHdBMguiGwrJaDpys4tIL0qwOpdOyR6VSXNXA3uP+2fPbcNp7vJLiqgayA3CIlItHppBfUkN+SU3HGxt+ySSIbvhk/ynAOdRxoJk3MgURzLg5fq75/zNvZIrFkXTexaOc7wtzjQUukyC64ZP9p8hMjbV00viuSo6NZNzABD49aN68/uzTg8WMHxhPcgCOoTU4KZqMlBhWm2ssYJkE0UWV9U1syivj0gC8e2h28chUdhaWU1rdYHUoRivKahrZWVgekMVLzS4emcrWgjNm8L4AZRJEFxWW1TIoMTqgh8/OHpWCKqw9ZHpV+6PPvixGlYC+xu69aBg5j14SUBXsxr94c0a5JSJSLCKtThcqIvNEpEJEdroej7VYt0BEDopIrog84q0Yu2NM/3hW/3Aek4f0sTqULhvb31l08ekBkyD80acHTpMcG8nY/vFWh9JlKXGRxEebiYQClddmlMM5U9yzwOvtbLNOVa9quUBEQoHngMuAImCLiHygqvu8FWh3+Ou4/O4ICRG+e8lw4nuZN7C/sdkdrP3yNJeNTiMkJHCvsWbHyuvoHx8V0O+XnshrdxCquhYo68KuU4FcVc1T1UZgKXCNR4PrpmPldazcezIoylVvnZHONRMGWB2GcZ7QEOFPd01lcRDM8bx89wlmPfUp+09UWR2K0UnizY5SIpIOLFPVsa2smwe8g/Mu4TjwQ1XdKyILgQWqerdru1uAaar6YBvnWAwsBkhLS8taunRpl2Ktrq4mNjbWrW0/zm/irwcb+d28XiRGebcapzNx+ZK/xgX+G1tPjau8wcFDq+u4PjOcq4e536G0p75e3dGV2LKzs7ep6uRWV6qq1x5AOrCnjXW9gVjX71cCh1y/3wC83GK7W4A/uHO+rKws7arVq1e7ve3N/7tRL//dZ10+V2d0Jq6uen9HkT781x2d2scXcXWVv8bWmbhsdof3AjmPL16vhc9/rr/8cG+n9gmG/6OvdSU2YKu28ZlqWSsmVa1U1WrX78uBcBFJxnlHMajFpgNx3mH4jclDElk0dVDHGwaIY+V1vLv9GMUBMF1qT3C6qoEJT/yD5btPWB2Kx7x17wz+/arRVodhdJJlCUJE+oqrxkpEprpiKQW2AJkiMlREIoCbgA+sirM1D182gttnDbU6DI+Zm+nspbs+14zu6g8+z3WO3jo4MdrqUDzGVE4HJq+1YhKRN4F5QLKIFAGPA+EAqvoCsBD4jojYgDrgJtftjk1EHgRWAqHAElXd6604DRjdrzd/WDSR2X4+n3ZPsbOwnMSYCEb36211KB6VW1xFcVVDQI1K29N5LUGo6qIO1j+Lsxlsa+uWA8u9EVd3PfHhXi4bnRZUF3lIiHD1+P5Wh2G4PH71aB7IHh4UzVtb+s/lB8gvqWH1D+dZHYrhJtOTuhMKSmp49fMCcourrQ7Fa9QM/205ESElLvDGXurInMxk8ktqKCyrtToUw00mQXTCOlcZ/ZzMwBtZsyO1jTYW/M9aXlmfb3UoPVphWS21jTarw/CKOZnOu25T1xU4TILohA25JQxI6EV6UvBUHjaLjgjD7lA++9IMu2Gln76/h2/8cYPVYXjFsJRY+sVHsc6M/RUwTIJwk8OhbMwrZeawpKBtkTE7M5nN+WVB0UM8ENU32dmUV8qMYUlWh+IVIsKczGTWHyrB7jBFmYHAJAg37TtRSXltE7OCuKXP3MwUGmwOthR0ZYQUo7u2FpyhweY42+w4GM3JTKGy3sYXReVWh2K4wSQIN33uKjedGaTf7gCmZSQSHiqsP2TKiK2wLvc04aHCtIxEq0PxmlnDkxGBdeYaCwgmQbjp88OlZKbGkto7yupQvCY6IoysIX1Ya968llh/qISsIX2IjvDmIMvWSoyJYGz/ePMlJECYBOGGBpudzfmlQV281Gz28GT2n6ikrKbR6lB6lPLaRvadqGRWEPWvacus4cnsKDwTtK21golJEG7YcbSc+iZHUBcvNZvh+oDKySu1OJKeZVN+GaowvUdcY0k02ZWtBWesDsXogEkQbtiQW0KIwLSM4H/zjhsYT3REKBsPmwThSzl5pUSFhzBuYODOHueuKel9CAsRNphrzO+ZBOGGzw+XMm5gQo+YeS08NISpQxPZcNiUEftSTl4ZWUP6EBkW/HM3R0eEMXFwAhvNXarfMwmiA9UNNnYVljNrePDfPTSbOSyJw6drOGWG//aJ8tpGDpysZPrQnnONzchIYndROZX1TVaHYrTDJIgObM4vxebQHlF52Kx5IEJTzOQbPan+odmMYck4FDbnmT43/qzdBCEiA0XkhyLydxHZIiJrReSPIvI1EekRyeXAySqiwkOYNKSP1aH4zAX9etM7KswkCB/ZeNhZ/zB+YILVofjMxMEJvHrHlKDtNR4s2mxwLSKvAgOAZcCvgWIgChgBLAB+KiKPqOpaXwRqlfvnDedb04YQFR78ZcPNQkOE6RlJbMgz9RC+kJNXyuQhiUSE9YjvXABEhYeSPTLV6jCMDrTXI+e/VHVPK8v3AO+6Znsb3NbOIrIEuAooVtWxraz/FvBj19Nq4Duqusu1rgCoAuyATduaUNtHekLl9Pnmj+lLdEQo9U32HpUcfe1MTSMHTlbxw8v7WR2KZRwODbq5L4JFe19ZFojIwLZWqmqjqua2s/9rOO802pIPXKSq44BfAi+dtz5bVSdYmRyOl9dRWt1g1ektdX3WQP7npokmOXhZiAj//rULuHxMX6tD8bmjpbXMeupTVuw9aXUoRhvaSxADgI2ueofviEinamldRU9t1kCp6gZVbe4pkwO0mYys8syqQ1z8X5/hMCNPGl4SHx3O3XMyGJEWZ3UoPtcvIYry2kZT1+XHpL0ZxMQ5rvVc4CbgGmAX8CbwnqpWdXhwkXRgWWtFTOdt90NglKre7XqeD5wBFHhRVc+/u2i572JgMUBaWlrW0qVLOwqrVdXV1cTGxp6z7Eef1TIwLoTvTbJu/KXW4vKVwioHO4ptXJ0R/pUhzq2MqyP+GpuJ66t+t7WeikbliZm9vrLOvF6d15XYsrOzt7VZUqOqbj2AUGA+sAOodXOfdGBPB9tkA/uBpBbL+rt+puJMSnPdOV9WVpZ21erVq895XtPQpNf/8XN9fUN+l4/pCefH5Utv5BzRIT9eprnFVV9ZZ2VcHfHX2M6Pq6y6QW95ZZNuLSi1JiAXK1+vU5V12mizt7ouUP6P/qQrsQFbtY3PVLeaTYjIhcAvgOeARuAnnUpRbR93HPAycI2qnr3PVNXjrp/FwHvAVE+crzOiI8L423dmcsuMdF+f2m80jz1ligC8Y1N+KWt7+Ax+qXFRhIf2nNZbgaa9Zq6ZOIuWFuFsTbQUuFxV8zxxYhEZDLwL3KKqX7ZYHgOEqGqV6/fLcSYnw8eGJEVz30XDGN2/t9WhBKWcvDJ6hYdy4YCe0/+hNXaHcqqynv4JXy1mMqzVXjPXlTjrG25U1d2dPbCIvAnMA5JFpAh4HAgHUNUXgMeAJOCPrvLt5uasacB7rmVhwF9UdUVnz99df995jCnpiT36ohURHrlilNVhBK3FczOYNzKlR/V/aM3339rJ9qNnWPdvF1sdinGeNhOEqma0fC4ivVtur6rt9pFX1UUdrL8buLuV5XnA+Pb29bbj5XV8b+lOfnbVaO6aPdTKUIwg1j+hV4/+AtJs4qAE/r7zOIVltQxKjLY6HKOFDr+6iMi9InIK+ALY5nps9XZgVtqU7yxznzY0eKd+dFeT3cHP3t/D+zuOWR1KUNF2Wg/2NDOHm7G//JU797Y/BMaoarqqDnU9MjrcK4Btyiujd1QYF/QzZe/hoSF89uVpPtp9wupQgsovl+1n0Us5JlEAmamxJMVEmEmq/JA7CeIwUOvtQPxJTl4pU4cmEWq6/wMwPSORzfllpsOgB32eW0JYqHylf0lPJOIc+ysnr9QkTD/jToJ4FNggIi+KyDPND28HZpWTFfUUlNYyPcMULzWbnpFERV0T+09WWh1KUCitbuDgqSqm94AZCt01PSOR4xX1FJbVWR2K0UJ7rZiavQh8CuwGHN4Nx3rN9Q/mzfsvza9FTl4ZY/oH/5SY3rYp39m+w1xj//Kva6yUwUmmotpfuJMgbKr6fa9H4idy8kqJM/UP5+if0IshSdFsPFxqWnV5QE5eKb3CQ3vE/NPuGp4aS3Kssx7im1MGWR2O4eJOEdNqEVksIv1EJLH54fXILJKTV8a0oYmm/uE804cmsTm/FLuph+i2nLxSJqf3MT2IWxARpmUksdHUQ/gVd67Qm3HVQxDkzVxPVdaTX1Jjbv1bMX1YIpX1NvafMPUQ3VFS3cCXp6rNNdaK6RlJnKio52hZj2oT49c6LGJS1R5TptDczM68eb+qZRnx2AGmaKSrNrvqH8xUm181w9UwJCevlCFJMRZHY0A7dxAiMru9HUWkt4i0O4x3oMnJKzP1D23oF9+L9KRocswk892Sk1dKdEQoF5ok+xXDUprrIcw15i/au4O4XkSeBlbgLFY6jXNO6uE4h+geAvzA6xH60Ka8Uqamm/qHtkzPSOKj3Se6VQ/R1NREUVER9fX1HozsXPHx8ezfv99rx++q+Ph4Lu7VxGVX9yX3y4NWh3OWt16vqKgoBg4cSHi4e1P2NtdDmP4Q/qO9sZgeFpE+wELgBqAfUIdz7oYXVXW9b0L0jfJ6B3kldSya2uY02z3e9Iwklm4p7FY9RFFREXFxcaSnp3utk4hqtK0AACAASURBVFhVVRVxcf43Q1t5RSVHqxz0jY8iNc66SajO543XS1UpLS2lqKiIoUPdL6WekZHER1+cMPUQfqLdOgh1Tgn6v65HUDtwxtnFw9Q/tK1lPcTwLh6jvr7eq8nBn9XbnT9jItxpXR7YRISkpCROn+7cfBdzM1O4f96wHj/Crb8w/wWXC5NDeeHbk8zcB+3oGx/F0OSYbo+Z0xOTA0C9TQkRoVdEqNWh+ERX/s+Dk6L5twWj6BdvRrn1ByZBuMSECwvG9jP1Dx2YnpFI0Zk6U0bcBXU2iIkMI6SHJkgj8JgEgRl6uTMev3oMH39vTsDeBRQUFDB27LmN737+85/z29/+ts19PvjgA5566qlundehSngo9I5yv3hp586dLF++vFPnSU9Pp6SkpLPhMW/ePLZu9Y/uTbnF1Tz8150U1wb9yD5+z535IKJF5Gci8r+u55kicpUb+y0RkWIR2dPGenEN/JcrIl+IyKQW6xaIyEHXukc68wd1xbIvTvCTdbUcKzcDhXUkKjw0YJNDV33961/nkUe6dxmGiJAWHUJSbKTb+3QlQQQDEXhvxzH2ldqtDqXHc+frzKs4m7nOcD0vAt4GlnWw32vAs8Drbay/Ash0PaYBzwPTRCQUeA64zHWuLSLygarucyPWLsnJK6WsXkmLc//N25PVNNgoqPDMm/fGFzee/f2v985odXlntDxGVzzzzDO88MILhIWFMXr0aJYuXcprr73G1q1befbZZ7n99tvp3bs3W7du5eTJkzz99NMsXLgQh8PBgw8+yGeffcbQoUNxOBzceeedLFy4sMPj19TU8P/+3/9j9+7d2Gw2fv7zn3PFFVfw2GOPUVdXx/r163n00Ue58cYbvxJvaWkpixYt4vTp00ydOvWcu+Frr72WwsJC6uvr+d73vsfixYux2+3cddddbN26FRHhzjvv5O67nRM7vv3229x///2Ul5fzyiuvMGfOHOx2O4888ghr1qyhoaGBBx54gHvvvZcTJ05w4403UllZic1m4/nnn2fOnDndeu2bZSTHkBIXyYEym0eOZ3SdOwlimKreKCKLAFS1Ttz4Cqmqa0UkvZ1NrgFeV+cVnSMiCSLSD0gHcl1TjyIiS13bei1BbDtyhhGJoYSZsXHc8h8f7ef97fXccrUGXZ3NU089RX5+PpGRkZSXl7e6zYkTJ1i/fj0HDhzg61//OgsXLuTdd9+loKCA3bt3U1xczAUXXMCdd955dp+S6gbErq0e/8knn+Tiiy9myZIllJeXM3XqVC699FJ+8YtfnE1MbXniiSeYPXs2jz32GB999BEvvfTS2XVLliwhMTGRuro6pkyZwvXXX09BQQHHjh1jzx7njX3Lv9Fms7F582aWL1/OE088wSeffMIrr7xCfHw8W7ZsoaGhgVmzZnH55Zfz7rvvMn/+fH76059it9uprfVcs1QR4T+vu5Djua0WPhg+5E6CaBSRXoACiMgwoMED5x4AFLZ4XuRa1tryaW0dREQWA4sB0tLSWLNmTacD+f6FyqlyW5f29bbq6mq/iyu+3kadDf704aekx3euRU58fDxVVVVnn79887/qA9pa3hlVVVXY7fZzjtVSTU0NDofjnPUNDQ1ERERQVVXF6NGjufHGG/na177GVVddRWhoKPX19TQ2NlJVVUVTUxPz58+npqaGQYMGcerUKaqqqvj000+5+uqrqampISYmhjlz5lBXV+eMx6Ecr3KQEEGrx1+xYgXvv/8+Tz/9NAB1dXXs37//nPO2Zc2aNfz5z3+mqqqKuXPnkpCQQHV1NZGRkfzmN79h2TLnjX5hYSE7d+4kMzOT3Nxc7r33XubPn88ll1yC3W7HbrezYMECqqqqGDlyJHl5eVRVVbF8+XL27NnDW2+9BUBlZSW7du1izJgx3H///VRXV3PVVVcxbty4VuOsr6/v0vUbDiSF1PndtQ/++Z5s5unY3EkQj+PsTT1IRN4AZgG3e+DcrX311HaWt0pVXwJeApg8ebLOmzevS8GsWbOGru7rTf4Y1+jKej7KX82IMePPzifsrv3793u9E1t7Hb+GDBlCRUXFOetramoYNWoUcXFxrFy5krVr1/LBBx/w29/+lr179xIVFUVERARxcXGEh4eTkJBwdn9VPbs8Kirq7PKwsDB69epFXFwc5bWNQC3R4SGtHl9EeO+99xg5cuQ5se7Zs+fsedsSEhJCXFzc2W1EhNjYWLZt28a6devYtGkT0dHRzJs3j9DQUAYPHszu3btZuXIlr776KsuWLeP3v/89oaGh9OnTh7i4OBoaGnA4HMTFxREaGspzzz3H/Pnzv3Lu9evX89FHH3Hffffxox/9iFtvvfUr20RFRTFx4sT2/2Ft8MdrH/w3LvB8bB2WqajqP4Fv4EwKbwKTVXWNB85dBLQc+H0gcLyd5YafSO0dxS9n9ep0cvAHsbGx9OvXj1WrVgFQVlbGihUrmD17Ng6Hg8LCQrKzs3n66acpLy+nurrarePOnj2bd955B4fDwalTp875FhcRGkJiTARh0vrx58+fzx/+8Iez9Qc7duwAIC4urt27B4C5c+fyxhtvAPDxxx9z5swZACoqKujTpw/R0dEcOHCAnJwcAEpKSnA4HFx//fX88pe/ZPv27e0ef/78+Tz//PM0NTUB8OWXX1JTU8ORI0dITU3lnnvu4a677urwOF3xyu4GfvLebo8f13Bfh3cQLVoXNc9aP1hE4oEjqtqdWqQPgAdddQzTgApVPSEip4FMERkKHANuwjnkuGF4xOuvv84DDzzAD37gHErs8ccfZ9iwYTQ1NfHtb3+biooKVJWHH36YhIQEt455/fXXs2rVKsaOHcuIESOYNm0a8fHOAfmiI8OIjgyjrKy+1eP/7Gc/46GHHmLcuHGoKunp6Sxbtozs7GyeeuopJkyY0GYl9eOPP86iRYuYNGkSF110EYMHO4eKWbBgAS+88ALjxo1j5MiRTJ8+HYBjx45xxx134HA4m5D+6le/avfvuvvuuykoKGDSpEmoKikpKbz//vusWbOG3/zmN4SHhxMbG8vrr7fVFqXrmhzKJ/tO8eS1Y3tcyzm/oartPoAcoBHnHBDbcNY/bAHygMvb2e9NnEmlCeddwV3AfcB9rvWCs7XSYZzTmU5use+VwJeudT/tKMbmR1ZWlnbV6tWru7yvN/lrXB//81P9+85jeqSkplP77du3z0sR/UtlZaXXz9GaqqoqVVUtKSnRjIwMPXHixDnrrYqrI96Mqzv/78df/4cO+fEyPVxc5cGIus9f35OqXYsN2KptfKa6UwdRANylqnsBRGQ08CPgl8C7wD/aSDyLOkhMCjzQxrrlQM9rAB5A6mzKw2/u4CdXjmLx3GFWh+MXrrrqKsrLy2lsbORnP/sZffv2paq+iep6G6m9TRPqzhqV6GwAkZNXRkZKrMXR9EzuJIhRzckBQFX3ichEVc0zt309V5+oEDKSY8jJKzMJwqW11iOVdU2U1zbRN757o7e++uqr/P73vz9n2axZs3juuee6dVx/lhYtpMZFkpNXys3TzCjLVnAnQRwUkeeBpa7nNwJfikgkzuIjo4eaPiyJD3cex2Z3mD4kbahusBMTGdbtMvQ77riDO+64w0NRBQYRYXqL+SHMF9KvUlX2n6hiVN84QrzQJ8mdd/XtQC7wEPAwzrqH23Emh2yPR2QEjOkZSVQ12NjXyfkhtIeMfdVkd9BgsxMT2TNGbz2fJ/7PM4YlUVzVQF5JjQciCj6Hiqu58pl1vLvjmFeO704z1zpV/S9VvU5Vr1XV36pqrao6VNW9NoBGUJo+1DmH8MbD7g//HRUVRWlpz5gxrKbB2cgvJjL45384n7omDIqK6l7RWss5SIyv2uR6Xaa53oue5k4z10zgV8BonFOOAqCqGV6JyAgYqb2jyEhxzg9x70Xu1UMMHDiQoqKiTk8k0xn19fXd/mDyhDO1jdQ12gmrjEJE/Cau83krruYpR7sjPSmatN6R5OSV8a1pQzwUWfDIyStjQEIvBvbxzvwZ7g7W9zjw3ziLlO6g9d7ORg80PSOJDzpRDxEeHt6pKSi7Ys2aNV3uvetJF//XGtKTYlhy+2jAf+I6n7/GBf+qh9hw2NRDnE9V2ZRfytzMFK+9Lu7UQfRS1VWAqOoRVf05cLFXojECzoyMJKobbOw93vV5qoNRcWU9eadrmJ7hnVv/nmR6RhKnTT3EVxw+XU1JdaNXp0l2J0HUi0gIcEhEHhSR64BUr0VkBJRprg9AU0Z8rpz8MsDMce4JM0w9RKs25jmvsWle/BLiToJ4CIgGvgtkAd8Gvjoql9EjpcZFMSyl+/NUB5ucvFLiIsMY0z/e6lAC3pCkaPr2jiLH9YFoOG3KK6VffBSDE6O9dg53EkS6qlarapGq3qGq1wOm14px1vSMJLYUnMFmN1NENss5XMrUoYlBN1+GFZz1EIln+0MYzvqHnLwypg1N9Gq9jDsJ4lE3lxk91HRXPcQeUw8BwKnKevJKakzxkgfNzkwhPSmayjozyxzA4dM1lFQ3eP0aa7MVk4hcgXPQvAEi8kyLVb0B818yzmrZVn3CIPdGPw1mzcVtJkF4zsKsgSzM6l6T2WCyKd8311h7dxDHcY7eWu/62fz4APjq7CFGj5USF8nw1FhTD+HSXP8wun9vq0MxglROXhlpvSMZkuS9+gdo5w5CVXcBu0Tkz9q9eR+MHuDpheNIjTMjlgIcOFnFtIwkU//gYScq6lh3qIQbsgb26P4QqsrGwyXMHp7s9dehvSKm3fxrHuqvrFfVcd4Lywg0kwb3sToEv/Hud2ZSWW++U3na2i9P8+N3djNpcALDU707ba0/O1Ts7P8wc5j3Z3Rsryf1VV4/uxF0HA71yqiSgUREiO8VbnUYQae5vH1jXlmPThAbcksA50CG3tZmHYSr1/QRVT2Csx7iQtejzrWsQyKyQEQOikiuiDzSyvofichO12OPiNhFJNG1rkBEdrvWbe3an2f40nff3ME9r/fsf9Xh09U02OxWhxGUBidG841JA+jfzbk1At3k9ER+NH8kg7zY/6GZO4P1fRP4DbAG5xhMfxCRH6nq3zrYLxTnlKKX4ZxydIuIfKCq+5q3UdXfuI6NiFwNPKyqLXvDZKtqSef+JMMq8b3CWbX/FE12B+E9cH4IVeWWlzeRlZ7IHxb559hGgUxE+N03J1gdhuXGDohn7ADfdMB05138U2CKqt6mqrcCU4GfubHfVCBXVfNUtRHnhEPXtLP9IpzzWBsBanpGErVNdg6d6pmjwB8preV4Rb3Xhl42DF+TjnomishuVb2wxfMQYFfLZW3stxBYoKp3u57fAkxT1Qdb2TYa513G8OY7CBHJB87grCh/UVVfauM8i4HFAGlpaVlLly5tbbMOVVdXExvrf/PeBlJcDXbF5oCYcGvrIKx6zQ6W2Vmyp4GHJkXRL/ar370C6X/pD1qLy+ZQ/lHQRP/YECakWjPPhpWv12dFTRwos3PX2EjCWqnr60ps2dnZ21R1cqsrVbXdB84ioJU4Z5G7HfgY+LUb+90AvNzi+S3AH9rY9kbgw/OW9Xf9TAV2AXM7OmdWVpZ21erVq7u8rzeZuDrPX2MzcXVOa3E5HA6d8Z+f6P1/3ub7gFysfL1uX7JJL/5t2+fvSmzAVm3jM9WdGeV+BLwIjAPGAy+p6o/dSExFwKAWzwfi7HzXmps4r3hJVY+7fhYD7+EssjICwMmKejMuk+EV589T3ZM02R1szi/zSfPWZh0mCBF5GNisqt9X1YdV9T03j70FyBSRoSISgTMJfNDK8eOBi4C/t1gWIyJxzb8DlwN73DyvYaFV+08x/Ver+OJYhdWh+NSJijr+sfckdY2mBZO3Tc9IorSmkUPFPauuK1SEv9wzndtmpvvsnO5UUvcGVorIOhF5QETS3DmwOntfP4izeGo/8Jaq7hWR+0TkvhabXgf8Q1VbzgaSBqwXkV3AZuAjVV3hznkNazWPxdTTht1Yuecki/+0jZLqBqtDCXo9dZ7qkBBh/KAEhqf6rv7DnSKmJ1R1DPAA0B/4TEQ+cefgqrpcVUeo6jBVfdK17AVVfaHFNq+p6k3n7ZenquNdjzHN+xr+Lyk2kpFpcT1u7P4Nh0sZlNjLJ23Te7pBib3oHx/V4xKEFUVqnWmsXgycBEoxM8oZ7ZiekcjWgjKaekg9hN2hbMovY2aG78qGezIRYfqwJHLyynpMPUR9k50pT67izc1HfXped+ogviMia4BVQDJwj5pxmIx2TM9IorbRzhdFPaMeYv+JSirqmnwy9IHhND0jibIeVA+x42g5JdUNpMT6dkBMd+4ghgAPuYp6HtcWPaENozVTh/aseao3HPbd2DiGU/M81RsP94xrbGNeKSECU704/3Rr3KmDeERVd/oiGCM4/Kseome8eTccLmVYSgxpvXv2GEG+NLBPLwYk9Oox19jGwyVcODCB3lG+HQSy5w2YY/iEsx7iTNDXQzS3TTd3D74lIkzLSGRTfhkOR3DXQ9Q22thxtPzsXZMvmQRheMWMYUnUNdnZVVhudShetauwnNpGu087LxlOM1z1EAdPVVkdildtzi/D5lBmWvAlxCQIwytmZCQTIrDuUHAPxrvuUAkiWPLm7elmZzqT8vogv8bWHyohIiyEKem+HwTSJAjDK+Kjwxk3MIF1h05bHYpXrTt0mnEDE0iIjrA6lB6nX3wvhqfGsjbor7ESpqYn0isi1OfnNgnC8Jq5mcnsLCynoq7J6lC8oqKuiZ2F5czNNMVLVpmTmcwXRRVBO/bXqcp6Dp6qYo5F15hJEIbXzBmRgkODtynixsOlOBTmZKZYHUqP9d2LM9n0k0sIC9IJqpqLaGebBGEEmwmDEoiNDAvaYqZ1h04TExHKxMEJVofSY/WJiSAq3PdFL76y/tBpkmMjuKBvb0vOb82MG0aPEB4awg8uH0F6UozVoXjFukMlzBiW1COnV/UnqsqR0lrSk4PrOnM4lPW5JcwenkxIK5MD+YJJEIZX3TFrqNUheM2rd0yh0RacZd+B5JX1+fzHR/vZ8tNLSYnz7VAU3rT/ZCUl1Y3MtrAI03z1MYwuGpYSywX9rLn1N/6leWiXz3ODq7lrc/2DVRXUYBKE4QMvfnaYJz8KriG8gr2HeCAZ0z+e9KRoymsbrQ7Fo9J6R3LdxAGWDuHi1QQhIgtE5KCI5IrII62snyciFSKy0/V4zN19jcBRUFrD0s2FQdMU0WZ3MONXq3jxs8NWh2IAoSHC6h/O4/YgK868buJA/vvGCZbG4LUEISKhwHPAFcBoYJGIjG5l03WqOsH1+EUn9zUCwJzMFKoabOwMkmE3dhWVU1LdyIA+vawOxXARsaYSN9h58w5iKpDrmh2uEVgKXOODfQ0/MzszmVdum8yY/vFWh+IRXxRVEBoizBlu+j/4kx1Hz7A5PzhmMnxrayHv7SiyOgzEWzMyichCYIGq3u16fgswTVUfbLHNPOAdoAg4DvzQNW91h/u2OMZiYDFAWlpa1tKlS7sUb3V1NbGxvpvr1V0mrs7zRWxVjUpcROe+tfrraxYscT32eR29wuDRad69s/P266Wq/NvaOvrHhvBwVufqH7oSW3Z29jZVndxmMN54ADcAL7d4fgvwh/O26Q3Eun6/Ejjk7r6tPbKysrSrVq9e3eV9vSmY4nI4HNpks3s+mPME02vmC8ES129WHNCMRz/S8ppG7wTk4u3X69CpKh3y42X6+saCTu/bldiArdrGZ6o3i5iKgEEtng/EeZdwlqpWqmq16/flQLiIJLuzrxFYSqobmPP0at7eZv1tc3ccKa2hrCa4WssEi+xRKdgdGvCD9yVEh/PTKy/g0gtSrQ7FqwliC5ApIkNFJAK4Cfig5QYi0ldctUsiMtUVT6k7+xqBJSkmAlX49ECx1aF0y5Mf7efrz65vvrM1/MiEQX3oEx3O6gC/xpJjI7lnbgb94q1vBOG1ntSqahORB4GVQCiwRJ31C/e51r8ALAS+IyI2oA64yXXL0+q+3orV8D4RIXtUCu9uP0aDzU5kWOCNn9Ngs7M+t4TrJg4wrWb8UGiIcNGIFNZ8eRq7Qwm1aHiKYOLVfhCqulxVR6jqMFV90rXsBVdyQFWfVdUxqjpeVaer6ob29jUCW/bIVGob7QHb0mRzfhm1jXYuHmX9rb/RuuxRqZTVNPJFUWA2qf5k3ylueWUTxZX1VocCmJ7Uhg/NHJZMZFhIwBYzrT5wmoiwEDP/tB+7aEQKIULAFjP9Y99JdhaW0yfGPyagMgnC8JleEaHMGJbEmoOBWYm4+mAxMzKSiI4wY1z6q4ToCCYN7sOnBwMvQagqqw+eZu6IFL8ZIdg/ojB6jOyRqeSX1JBfUmN1KJ3SHHP2SNM5zt9lj0plz7FKvymmcdfe45Wcrmoge6T/FGGaBGH4VHP5/Sf7TlkcSec0x3vJBWkWR2J05Ow1tj+w7iL+ue8UIjDPj76EmARh+NSgxGjG9O/Nir0nrQ6lU1bsPcnofr0ZlBhtdShGB0b1jWNIUnTAXWMr955kSnoiybH+M6eFSRCGzy0Y05dtR84ETBFAcWU9246cYcHYvlaHYrhBRFgwpi8bckuoqGuyOhy35JfUcOBkFQvG+Nc1ZhKE4XPNH7QrA6SYqTlOkyACx/yxfbE5NGBaM6103e3M97NrzCQIw+eGp8aSkRLDyj2BUQSwcs9JMpJjyEz1vwHtjNZNGJhAWu9I1gRIa6YVe04ybmA8AxKs7z3dkkkQhs+JCPPH9GVjXqnfzwJWXttITl4p88f2Nb2nA0hIiLB08Qx+e8N4q0Pp0ImKOnYWljPfz4qXwCQIwyILxvTF7lC/b2myan8xNof6Xdmw0bGhyTGE+Ul/gvb8Y6+zCNMkCMNwGTcwnsVzMxiZFmd1KO1asfck/eKjGDcwOCY76mnsDvX7Pjcr9pxkeGosw/2wCNN0CTUsISL85MoLrA6jQ09eO5YjZbWmeClAPfHhXt7bcYxt/34ZEWH+9324rKaRTfml3D9vuNWhtMr/XjHD8COpvaOYkp5odRhGF83NTKGq3sbnh0usDqVV/9x3Eof6Z/ESmARhWOzZTw/xyDtfWB1GqypqA6MNvdG2OSOSGZocwxk/neQpOTaSq8b1Y+yA3laH0iqTIAxLVdbbeGd7kd+9gc/UNDLlPz/hTxsLrA7F6IbIsFA+/cFFfGPSQKtDadUlF6Tx7M2T/LYI0yQIw1LXThhAk139bliEj3afoNHmYNKQPlaHYnSTv374BgKvJggRWSAiB0UkV0QeaWX9t0TkC9djg4iMb7GuQER2i8hOEdnqzTgN61zQL4637p3BNycP6nhjHyooqWFU3zhG9/PPW3+jc3YWlrNkfb7VYZzjB2/t4h9+9sXofF5rxSQiocBzwGVAEbBFRD5Q1X0tNssHLlLVMyJyBfASMK3F+mxV9c/aJcMjRISpQ/2vEvjfrxpNg81uvn0GiX/uO8nzaw5z1fh+pMZFWR0O+45X8s72IiYM8u/m0968g5gK5Kpqnqo2AkuBa1puoKobVPWM62kO4J8FhYbX2R1K0Zlaq8M4RyDOm2207rqJA3AofLjrhNWhAPD+zmOEhQhfG9ff6lDaJarqnQOLLAQWqOrdrue3ANNU9cE2tv8hMKrF9vnAGUCBF1X1pTb2WwwsBkhLS8taunRpl+Ktrq4mNtb/Oqr0lLie3VFPUZWDX83p1e1v7d2JTVX5rMjGhNRQEiI9+/2pp/wvPcXTcf18Q53z58zujXfkibgqG5TDFXYmpnq2EKcrsWVnZ29T1cmtrlRVrzyAG4CXWzy/BfhDG9tmA/uBpBbL+rt+pgK7gLkdnTMrK0u7avXq1V3e15t6SlxLNx/RIT9epjuOnun2sboT286jZ3TIj5fp0s1Huh3H+XrK/9JTPB3Xy+vydMiPl+mhU5XdOo6/vl6qXYsN2KptfKZ6s4ipCGhZ8zgQOH7+RiIyDngZuEZVS5uXq+px189i4D2cRVZGkLriwn5EhoXw1tZCS+P469ZCIsNCWDC2n6VxGJ739fH9CQsR3tpaZGkcZX7WpLs93kwQW4BMERkqIhHATcAHLTcQkcHAu8Atqvpli+UxIhLX/DtwObDHi7EaFusdFc5V4/rz9x3HqGmwWRJDTYONv+84xlXj+hPfK9ySGAzvSYmL5NIL0vjbtiIabHZLYiiraWTGr1bxfxsKLDl/Z3ktQaiqDXgQWImz+OgtVd0rIveJyH2uzR4DkoA/ntecNQ1YLyK7gM3AR6q6wluxGv7h5mmDqWm088Gur9xo+sQHu45T02jn5mmDLTm/4X03TxtMWU0jK/daM1nVO9uKaLA5mJ6RZMn5O8urg/Wp6nJg+XnLXmjx+93A3a3slwf4/0DuhkdNGpzAqL5xvLHpCDdNGeTTJqaqyl82HWVU3zgmDU7w2XkN35o9PJlBib14I+cIXx/v2xZEDofy5uajZA3pw8i+/j2KcTPTk9rwGyLCt6YPYc+xSrYfPdPxDh60/egZdh+r4FvTBpu+D0EsJES4eeoQNuWXsf9EpU/PvS63hLySGr4VQHeoJkEYfuX6SQPoHRXGkvUFPj3vkvUF9I4K89sxewzPWTR1EL3CQ3n1c9/2rF6yPp+UuEiu8vO+Dy2ZBGH4leiIMBZNG8zHe074rONc0ZlaPt5zgkXTBhMTaaZICXYJ0RFcnzWA93cep6S6wSfnzC2u4rMvT3Pr9CF+OS9FWwInUqPHuG1GOiLis7uIVz8vQES4dUa6T85nWO/2mUNptDl43UetiV5el09kWEjANYAwCcLwO/0TenHthAH8ZfMRSr38Da+0uoE3Nh3hmgn9GZDQvR62RuAYnhrL5aPTeG1DAVX13p3341h5He9sL+KmKYNIio306rk8zSQIwy/dnz2MBpuDV7w8Aucr6/NpsDn8dspHw3v+38WZLJo2XZ5QwQAAC59JREFUGId3Rhs666XPDqMKiy8a5t0TeYFJEIZfGpYSy5UX9uP1jUe81vO0rKaR1zce4cqx/fxywnjDuy4cGM+jV1zg1U6RJyvqWbqlkOsnDQzIO1STIAy/9dAlmSTHRnitsvrZT3OpbbTxvUszvXJ8I3BUeqmY6b//+SWq8ODFgXmHappsGH4rMy2OT38wj5AQ7/RL+Nq4vvSLj2JEWmB0WjK8460thfzyo32s+sFFHp0r4tCpKt7eVsjtM4cyKDHaY8f1JXMHYfi15uTg8EJBcdaQRO6Zm+Hx4xqBZerQROoa7Ty94qDHjqmq/GLZPmIiwgL27gFMgjACwMmKeq774+es2u+Z8XNyi6uob7JmsDbD/6Qnx3DP3AxOVNTRaHN45JhNdiUjOYZ/WzCSxJgIjxzTCqaIyfB7iTER1Dbaeezve5mWkURsNzqzVTfYuP3VLYxMi+OV26d4MEojkH3/shGEhYjHhlmJCAvhiWvGeuRYVjJ3EIbfiwgL4anrL6S0poHtR7o3RtNj7+/heHkd35kXeE0ODe8JDw05mxyquzHcvKqSd7raU2FZziQIIyBkDUnk8x9fzNwRKV0+hqoyPSOJH80fxeT0RA9GZwSLgyeruOjp1Xy8u2tzV7++8Qjz/2ctXxSVezgya5giJiNgtOyFeqKijn7xnWtXLiJ8c8qgjjc0eqyhyTEMSozm4bd20jc+iomD+7i975qDxTzx4V6yR6Yytn+8F6P0HXMHYQScl9flcfnv1rI5v8yt7bcUlLF081EvR2UEg4iwEF6+bTKpcVHc+dqWTt0JjEiL49oJA3hm0USvNc32Na8mCBFZICIHRSRXRB5pZb2IyDOu9V+IyCR39zV6risv7EdK70i+/com3tlWhHPe9a9SVd7dXsS3Xt7ES+vyvD7mjhEckmMjef3OqcREhnHTSzksb6e4SVUpr3X29O+f0Ivf3TghqEYE9lqCEJFQ4DngCmA0sEhERp+32RVApuuxGHi+E/saPVT/hF68c99MJgxK4Adv7+K2V7ew/lDJ2b4SDlU+zy3htle38P23djFhYALv3DeTuCgzz7ThnvTkGN69fyaZqbHc/8Z27v6/reTklZ69xuwOZc3BYhb9bw53vLbFK/10/IE3U91UINc1fSgishS4BtjXYptrgNfV+RUwR0QSRKQfkO7GvkYP1icmgjfvmc6fNhbw358c4tuvbCIyLITk2EiKK+tocmwivlc4j189mltnpBMaJLf8hu+kxkXxt+/M5JX1+Tz3aS6f7D9FVHgIMaFK1T9X0Gh3kBgTwY/mj7Q6VK+Rtm7Pu31gkYXAAte804jILcA0VX2wxTbLgKdUdb3r+SrgxzgTRLv7tjjGYpx3H6SlpWUtXbq0S/FWV1cTG+t/A7aZuDrWaFd2FtvJq3BQ0eAgOsTGiOQoJqaGEhHqP4nBn16zlkxcHWuwK9tO2TlSaaesponk2AiGxYcwITWUMD/68tGV1yw7O3ubqk5ubZ037yBae9XOz0ZtbePOvs6Fqi8BLwH8//buNVaOso7j+PenFgxIRGzUcrNYIEaQXmwQWzSHN1JroJhIUiQSkYQUqNEYSRpf1Fcak75RX5CGmIaQKMYbhGK5SKTWtLZYoaVtqqbcm5KUeinFEsiBvy9m1jMus7sz231mNuvvk5x0Ls+z+zuT/+mzs7P7zOLFi2NqaqpGxBmbN29m2L4pOVc1ny0sj1u2DueqZ9xyXZn/O265ikadLeUAcRAofqbwbOBQxTYnVehrZmYJpfwU05+ACySdJ+kkYCVwf1eb+4Eb8k8zXQYcjYiXKvY1M7OEkp1BRMS0pNXAw8A7gQ0RsU/Sqnz/emATsBw4ABwHbuzXN1VWMzN7u6Qf2I2ITWSDQHHb+sJyALdV7WtmZs3xN6nNzKyUBwgzMyvlAcLMzEp5gDAzs1LJvkndBkkvA88P2X02cGSEcUbFueob12zOVY9z1TdMtg9HROmNViZqgDgRknb2+rp5m5yrvnHN5lz1OFd9o87mt5jMzKyUBwgzMyvlAWLGnW0H6MG56hvXbM5Vj3PVN9JsvgZhZmalfAZhZmalPECYmVmpiR8gJC2T9FdJByStKdkvST/K9z8laVHVvolzXZ/neUrSNknzC/uek7RH0i5JO0eZq2K2KUlH8+ffJWlt1b6Jc91eyLRX0puSzsj3JTtmkjZIOixpb4/9bdXYoFyt1FiFXG3V16BcbdXXOZIek7Rf0j5JXy9pk6bGImJif8imCn8a+AjZTYh2Ax/rarMceJDsLnaXATuq9k2cawnwvnz5c51c+fpzwOwWj9kU8MAwfVPm6mp/FfC7ho7ZZ4BFwN4e+xuvsYq52qqxQbkar68quVqsrznAonz5NOBvTf0/NulnEJcCByLimYh4A/gZsKKrzQrg7shsB06XNKdi32S5ImJbRPwzX91Odle9JpzI793qMetyHXDPiJ67r4jYAvyjT5M2amxgrrZqrMLx6qXV49Wlyfp6KSKeyJePAfuBs7qaJamxSR8gzgJeLKwf5O0HtlebKn1T5iq6iezVQUcAj0j6s6SbR5SpbrZPSdot6UFJF9XsmzIXkk4BlgG/KmxOecwGaaPG6mqyxqpour4qa7O+JM0FFgI7unYlqbGkNwwaAyrZ1v253l5tqvQdVuXHlnQF2R/v5YXNSyPikKQPAL+V9Jf81U9T2Z4gm7/lVUnLgfuACyr2TZmr4ypga0QUXw2mPGaDtFFjlbVQY4O0UV91tFJfkt5DNih9IyJe6d5d0uWEa2zSzyAOAucU1s8GDlVsU6VvylxIugT4MbAiIv7e2R4Rh/J/DwP3kp1GjsrAbBHxSkS8mi9vAmZJml2lb8pcBSvpOv1PfMwGaaPGKmmpxvpqqb7qaLy+JM0iGxx+EhG/LmmSpsZSXFQZlx+yM6RngPOYuUBzUVebz/O/F3cer9o3ca5zye7VvaRr+6nAaYXlbcCyho/Zh5j5kuWlwAv58Wv1mOXt3kv2PvKpTR2z/HHn0vuia+M1VjFXKzVWIVfj9VUlV1v1lf/udwM/6NMmSY1N9FtMETEtaTXwMNnV/A0RsU/Sqnz/erL7Xi8n+0M5DtzYr2+DudYC7wfukAQwHdksjR8E7s23vQv4aUQ8NIpcNbJ9EbhF0jTwGrAysmps+5gBfAF4JCL+Xeie9JhJuofskzezJR0EvgPMKuRqvMYq5mqlxirkary+KuaCFuoLWAp8GdgjaVe+7dtkA3zSGvNUG2ZmVmrSr0GYmdmQPECYmVkpDxBmZlbKA4SZmZXyAGFmZqU8QJj1IOl0SbcW1s+U9MtEz3VNcdbSkv0fl3RXiuc268UfczXrIZ/35oGIuLiB59oGXB0RR/q0eRT4akS8kDqPGfgMwqyf7wPz8jn+10ma27lXgKSvSLpP0kZJz0paLembkp6UtL1wn4B5kh7KJ3H7g6SPdj+JpAuB1zuDg6Rr8/sN7JZUnM9nI9k0D2aN8ABh1tsa4OmIWBARt5fsvxj4Etl0EN8FjkfEQuCPwA15mzuBr0XEJ4BvAXeUPM5SsgnqOtYCV0bEfODqwvadwKdP4Pcxq2Wip9owS+yxyObnPybpKNkrfIA9wCX57JtLgF/k0zAAnFzyOHOAlwvrW4G7JP0cKE7Mdhg4c4T5zfryAGE2vNcLy28V1t8i+9t6B/CviFgw4HFeI5sEDoCIWCXpk2QTsO2StCCymVbfnbc1a4TfYjLr7RjZLR6HEtmc/c9Kuhb+e9/g+SVN9wPnd1YkzYuIHRGxFjjCzHTNFwKl90s2S8EDhFkP+av2rfkF43VDPsz1wE2SdgP7KL/d4xZgoWbeh1onaU9+QXwL2RTNAFcAvxkyh1lt/pir2RiQ9ENgY0Q82mP/ycDvgcsjYrrRcPZ/y2cQZuPhe8ApffafC6zx4GBN8hmEmZmV8hmEmZmV8gBhZmalPECYmVkpDxBmZlbKA4SZmZX6D6ijTy9IkIpSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(t,s,dashes=[2,2,10,2],label=\"Using set_dashes\")\n",
    "ax.legend()\n",
    "ax.set(xlabel='time (s)', ylabel='voltage (mV)', \n",
    "           title='Voltage / Time')\n",
    "ax.grid()\n",
    "fig.savefig(\"test.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CgEtSowy4JDXKgEtSowy4JDXKgEtSowy4JDXq/wBO3/81X39u4QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "#创建数据\n",
    "classA = [41,45,15]\n",
    "classB = [30,65,15]\n",
    "range=[0,25,50]\n",
    "#画图\n",
    "plt.scatter(range,classA, color='r')\n",
    "plt.scatter(range,classB, color='g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
